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Machine Vision and Applications
Published in: Machine Vision and Applications 1/2021

01-02-2021 | Original Paper

Online inspection of narrow overlap weld quality using two-stage convolution neural network image recognition

Authors: Rui Miao, Zihang Jiang, Qinye Zhou, Yizhou Wu, Yuntian Gao, Jie Zhang, Zhibin Jiang

Published in: Machine Vision and Applications | Issue 1/2021

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Abstract

In narrow overlap welding, serious defects in the weld will lead to band breakage accident, and the whole hot dip galvanizing unit will be shut down. Laser vision inspection hardware is used to collect real-time image of weld surface, and image defect recognition and evaluation system is developed to real-time detect quality. Firstly, region division is implemented. In view of the characteristics of gray image such as large information, low contrast and blurred edge, an improved image segmentation algorithm is proposed by using image convolution to enhance edge features and combining with integral image, which can quickly and accurately extract the weld edge and divide the region, and the processing time can meet the real-time requirements. Then comparing the effect of traditional method and convolution neural network in identifying defects, VGG16 is used to identify weld defects. In order to ensure real-time performance, a two-stage weld defect recognition is proposed. First, the large defective area is identified, and then, the defect is accurately identified in the area. This method can quickly extract defect areas and complete weld defect classification. Experiments show that the accuracy can reach 97% and average running time takes 3.2 s, meeting the online detection requirements.
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Metadata
Title
Online inspection of narrow overlap weld quality using two-stage convolution neural network image recognition
Authors
Rui Miao
Zihang Jiang
Qinye Zhou
Yizhou Wu
Yuntian Gao
Jie Zhang
Zhibin Jiang
Publication date
01-02-2021
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 1/2021
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-020-01158-2

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